Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/354700
COMPARTIR / EXPORTAR:
SHARE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | New method for the automated massive characterization of Bias Temperature Instability in CMOS transistors |
Autor: | Saraza-Canflanca, P. CSIC ORCID; Diaz-Fortuny, J.; Castro-López, R. CSIC ORCID ; Roca, E.; Martin-Martinez, J.; Rodriguez, R.; Nafria, M.; Fernandez, F. V. | Palabras clave: | Aging | Bias Temperature Instability | Characterization | Maximum Likelihood Estimation | Reliability | Time Dependent Variability | Fecha de publicación: | 14-may-2019 | Editor: | Institute of Electrical and Electronics Engineers | Resumen: | Bias Temperature Instability has become a critical issue for circuit reliability. This phenomenon has been found to have a stochastic and discrete nature in nanometer-scale CMOS technologies. To account for this random nature, massive experimental characterization is necessary so that the extracted model parameters are accurate enough. However, there is a lack of automated analysis tools for the extraction of the BTI parameters from the extensive amount of generated data in those massive characterization tests. In this paper, a novel algorithm that allows the precise and fully automated parameter extraction from experimental BTI recovery current traces is presented. This algorithm is based on the Maximum Likelihood Estimation principles, and is able to extract, in a robust and exact manner, the threshold voltage shifts and emission times associated to oxide trap emissions during BTI recovery, required to properly model the phenomenon. | Descripción: | Talk delivered at the 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), in Florence (Italy), 25 – 29 March 2019. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | Versión del editor: | https://ieeexplore.ieee.org/document/8715029 | URI: | http://hdl.handle.net/10261/354700 | DOI: | 10.23919/DATE.2019.8715029 | ISBN: | 9783981926323 |
Aparece en las colecciones: | (IMSE-CNM) Comunicaciones congresos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
DATE2019_New method for the automated massive.pdf | 1,4 MB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
Page view(s)
17
checked on 21-may-2024
Download(s)
4
checked on 21-may-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.